Google Cloud is committed to providing a full spectrum of ML accelerators, including both Cloud GPUs and Cloud TPUs. While some custom silicon chips can only perform a single function, TPUs are fully programmable, which means that Cloud TPU Pods can accelerate a wide range of state-of-the-art ML workloads, including many of the most popular deep learning models. For example, a Cloud TPU v3 Pod can train ResNet-50 (image classification) from scratch on the ImageNet dataset in just two minutes or BERT (NLP) in just 76 minutes. Cloud TPU customers see significant speed-ups in workloads spanning visual product search, financial modeling, energy production, and other areas. In a recent case study, Recursion Pharmaceuticals iteratively tests the viability of synthesized molecules to treat rare illnesses.

Most enterprises already see the value of AI. In fact, more than 60 percent are in the process of adopting it right now. What we found, working with hundreds of enterprises, is that using AI comes down to simplicity and usefulness. Enterprises need tools that are simple and familiar, and they need to be able to directly apply them to their unique challenges. Today, we're making a number of updates to our data analytics and Cloud AI services aimed at making AI more simple and useful, and putting it in the hands of as many businesses and developers as we can.